Sentiments based transaction systems and methods

ABSTRACT

Systems and methods of facilitating transactions related to targeted or customized commercial offerings based on derived sentiment states are provided. The sentiment states are derived from digital representations such as images, videos and sound recordings.

This application is a continuation application of U.S. application Ser.No. 17/061,443, filed Oct. 1, 2020, which is a continuation applicationof U.S. application Ser. No. 16/566,712, filed Sep. 10, 2019, which is acontinuation application of U.S. application Ser. No. 14/596,090, filedJan. 13, 2015, which claims priority to U.S. provisional applicationSer. No. 61/926,512, filed Jan. 13, 2014. The contents of theseapplications and all other extrinsic materials referenced herein arehereby incorporated by reference in their entirety.

FIELD OF THE INVENTION

The field of the invention is computer-based targeted transactionfacilitating systems and methods.

BACKGROUND

The following description includes information that may be useful inunderstanding the present invention. It is not an admission that any ofthe information provided herein is prior art or relevant to thepresently claimed invention, or that any publication specifically orimplicitly referenced is prior art.

Targeted advertising, a type of advertising wherein advertisements areplaced to reach certain groups of consumers based on demographics,behavioral variables and some other traits, has existed in limited formsfor several years. Unfortunately, targeted advertising has beencontroversial due to privacy concerns, and the effectiveness of targetedadvertising is questionable due to changes in, and decreasingpredictability of, societal behavior.

One field in which long term changes in behavior do not have a greatimpact relates to understanding a person's current sentiments based onimage data or other digital data. Examples of efforts in this fieldinclude Japanese patent publication no. 2001/087559 to Murata relatingto determining a mental state from an image of a user, and U.S. Pat. No.8,462,996 to Moon et al. relating to methods of determining a person'semotional response to a visual stimulus based on the person's facialexpression.

These and all other publications identified herein are incorporated byreference to the same extent as if each individual publication or patentapplication were specifically and individually indicated to beincorporated by reference. Where a definition or use of a term in anincorporated reference is inconsistent or contrary to the definition ofthat term provided herein, the definition of that term provided hereinapplies and the definition of that term in the reference does not apply.

Unfortunately, previous efforts have apparently failed to appreciatethat an understanding of a person's sentiments could be used tocustomize commercial offerings and facilitate transactions.

Thus, there is still a need for improved targeted transactionfacilitating systems and methods.

SUMMARY

The inventive subject matter provides computer-based apparatus, systemsand methods in which a targeted or customized commercial offering can beprovided based at least in part on a sentiment derived from a digitalrepresentation of a scene. Some contemplated systems can advantageouslyrecognize objects or people in a scene, derive characteristicsassociated with the scene and infer a sentiment state of a person in thescene or a person viewing the scene. Additionally or alternatively, thesystem could map the inferred sentiment to one or more associatedcommercial offerings, present them to a user and facilitate atransaction between the user and a vendor associated with the commercialoffering.

Viewed from another perspective, an exemplary system of the inventivesubject matter can include a digital sensor, one or more databasesstoring sentiment states and associated commercial offerings, and one ormore engines configured or programmed to (1) obtain digital data fromthe digital sensor, (2) query the one or more databases using thedigital data to determine one or more sentiment states and one or moreassociated commercial offering, and at least one of (3) provide thecommercial offering(s) to a user, and (4) facilitate a transactionbetween the user of the system and a vendor related to the commercialoffering(s).

In some aspects of the inventive subject matter, a communicationssubsystem can provide a link to at least one of an offeree and a vendorto facilitate one or more transactions. Some exemplary transactionsinclude a sale, a purchase, a license, a lease, a preview, a download, avote, a sale, or an exchange. The communications subsystem canadditionally or alternatively facilitate a by executing the transactionor by causing the transaction to be executed.

Various objects, features, aspects and advantages of the inventivesubject matter will become more apparent from the following detaileddescription of preferred embodiments, along with the accompanyingdrawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an ecosystem including a system of the inventivesubject matter.

FIGS. 2A-2B illustrate a system for providing commercial offeringswherein multiple sentiment characteristics are determined from digitaldata.

FIGS. 3A-3B illustrate a table including non-limiting examples of scenedata, sentiment characteristics, and sentiment states.

DETAILED DESCRIPTION

The following discussion provides many example embodiments of theinventive subject matter. Although each embodiment represents a singlecombination of inventive elements, the inventive subject matter isconsidered to include all possible combinations of the disclosedelements. Thus if one embodiment comprises elements A, B, and C, and asecond embodiment comprises elements B and D, then the inventive subjectmatter is also considered to include other remaining combinations of A,B, C, or D, even if not explicitly disclosed.

It should be noted that any language directed to a computer should beread to include any suitable combination of computing devices, includingservers, interfaces, systems, databases, agents, peers, engines,controllers, modules, or other types of computing devices operatingindividually or collectively. One should appreciate the computingdevices comprise a processor configured to execute software instructionsstored on a tangible, non-transitory computer readable storage medium(e.g., hard drive, FPGA, PLA, solid state drive, RAM, flash, ROM, etc.).The software instructions preferably configure or program the computingdevice to provide the roles, responsibilities, or other functionality asdiscussed below with respect to the disclosed apparatus. Further, thedisclosed technologies can be embodied as a computer program productthat includes a non-transitory computer readable medium storing thesoftware instructions that causes a processor to execute the disclosedsteps associated with implementations of computer-based algorithms,processes, methods, or other instructions. In some embodiments, thevarious servers, systems, databases, or interfaces exchange data usingstandardized protocols or implementations of algorithms, possibly basedon HTTP, HTTPS, AES, public-private key exchanges, web service APIs,known financial transaction protocols, or other electronic informationexchanging methods. Data exchanges among devices can be conducted over apacket-switched network, the Internet, LAN, WAN, VPN, or other type ofpacket switched network; a circuit switched network; cell switchednetwork; PSTN; or other type of network.

As used in the description herein and throughout the claims that follow,when a system, engine, server, device, module, or other computingelement is described as configured to perform or execute functions ondata in a memory, the meaning of “configured to” or “programmed to” isdefined as one or more processors or cores of the computing elementbeing programmed by a set of software instructions stored in the memoryof the computing element to execute the set of functions on target dataor data objects stored in the memory.

The focus of the disclosed inventive subject matter is to enableconstruction or configuration of a computing device to operate on vastquantities of digital data, beyond the capabilities of a human. Althoughthe digital data in disclosed embodiments represent sentiment orsentiment states, it should be appreciated that the digital data is arepresentation of one or more digital models of a sentiment state, not ahuman's sentiment itself. By instantiation of such digital models in thememory of the computing devices, the computing devices becomespecialized to manage the digital data or models in a manner that thecomputing devices lacked a priori and that could provide utility to auser of the computing device that the user would lack without such atool.

The inventive subject matter provides apparatus, systems and methods inwhich a targeted or customized commercial offering can be provided basedat least in part on a sentiment derived from a digital representation ofa scene. Some contemplated systems can advantageously recognize objectsand people in a scene, derive characteristics associated with the sceneand infer a sentiment state of a person in the scene or a person viewingthe scene. Additionally or alternatively, the system could map theinferred sentiment to one or more associated commercial offerings,present them to a user and facilitate a transaction between the user anda vendor associated with the commercial offering. Viewed from anotherperspective, a system of the inventive subject matter can providecommercial offerings to users based at least in part on a sentiment ofthe user or a person in the user's environment.

One should appreciate that the inventive subject matter allows targetedor customized commercial offerings to be provided, and for transactionsto be facilitated, initiated and executed, based on a sentiment derivedfrom a digital representation, optionally in real time or near realtime. Further, the disclosed sentiment analysis techniques relate tocomputing devices that, a priori, lack capabilities of understanding,let alone taking action on, modeled sentiment. Still further, thedisclosed techniques focus on configuring computing devices to modelsentiments based on one or more digital data modalities (e.g., digitalimages, digital video, digital sound, etc.) One should furtherappreciate that the manipulation of such digital data constructs inorder to model sentiment exceeds the capacity of a human begin toanalyze the digital data.

An exemplary system, such as the system shown in the ecosystem of FIG. 1, can include a digital sensor (e.g., the sensor of device 110, etc.),one or more databases storing sentiment states and associated commercialofferings (e.g., commercial offerings 140 and 160, etc.), and one ormore engines (e.g., sentiments analysis engine 130 and commerce analysisengine 150, etc.) programmed to (1) obtain digital data via the digitalsensor, (2) query the one or more databases using the digital data todetermine one or more sentiment states and one or more associatedcommercial offering, and at least one of (3) provide the commercialoffering(s) to a user, and (4) facilitate a transaction between the userof the system and a vendor related to the commercial offering(s). Someor all of the components of a system of the inventive subject matter canbe implemented as software modules that when executed by one or moreprocessing units (e.g., a processor, a processing core, etc.) performfunctions and fulfill the roles or responsibilities described herein.

The “digital data” that is obtainable via the digital sensor can includea digital representation of an environment or scene. For example, thedigital representation can include one or more images, videos, or audioclips/sounds. In some embodiments, when a digital data derivation engineobtains the digital representation from the digital sensor, the digitaldata derivation engine extracts scene data from the digitalrepresentation.

Additionally or alternatively, digital data can include scene data thatis derived from the digital representation, for example, by executing animplementation of an object recognition algorithm (e.g., SIFT, FREAK,DAISY, FAST, etc.) on a set of images received from a differentcomputing device.

Where an implementation of an image recognition algorithm is executed,descriptor sets or other quantified feature sets can be obtained asscene data. The term “descriptor” is used euphemistically to mean a datastructure stored in memory where the values in the data structure arederived by executing one or more implementations of algorithms (e.g.,object recognition algorithm, etc.) on a digital representation of anobject or scene stored in the memory. Descriptors might represent localor global features in the digital representation (e.g., edges, corners,etc.). Descriptors could also represent specific measures associatedwith patches of the image (e.g., SIFT descriptors, Histogram ofGradients, etc.). One can use an implementation of an image recognitionalgorithm such as scale-invariant feature transform (SIFT; see U.S. Pat.No. 6,711,293 titled “Method and apparatus for identifying scaleinvariant features in an image and use of same for locating an object inan image” filed Mar. 6, 2000) to detect and describe local features (asdescriptors) in images. A typical SIFT descriptor can be a 128-bytevector that represents a 128-bin histogram of gradient orientations. Aglobal descriptor could comprise a histogram with thousands of bins;Vector of Locally Aggregated Descriptors (VLAD) for example. Multipledescriptors can be derived from a single image. As such, each distinctimage of an object can be associated with a set of descriptors thatuniquely defines the different features of the object. In someembodiments, an engine of a system can recognize objects that arerepresented in a digital representation (e.g., an image, etc.) based onthe descriptors derived from the digital representation and the knownassociations between the descriptors and the objects.

The scene data that is obtained by (or derived from a digitalrepresentation by) the one or more engines can be used to determine oneor more sentiment characteristics based on a query of a database and aset of rules for associating sets of scene data with sentimentcharacteristics.

FIGS. 3A-3B illustrate a table including some non-limiting examples of“scene data,” “sentiment characteristics,” and “sentiment states” asused in the description and claims herein. Sentiment states couldinclude any feeling, emotion, view, opinion, attitude, thought or beliefthat is associated with at least one of scene data and one or moresentiment characteristics. At a basic level, sentiment includes arepresentation that indicates an individual might have a feeling that ispositive, negative, or neutral toward as topic or item of interest.Still, in more complex embodiments, this can be a feeling, emotion,view, opinion, attitude, thought or belief associated with a person inthe scene, or a feeling, emotion, view, opinion, attitude, thought orbelief associated with a person viewing or capturing the scene.

It should be appreciated that the subject matter of FIGS. 3A-3B isrepresented by digital data constructions in the memory of the disclosedcomputing devices. For example, scene data can comprise digitalidentifiers (e.g., GUID, UUID, Hash values, etc.) that represent eachtype of item or object in in scene. The sentiment characteristics cancomprise attribute-value pairs that represent specific features ormeasurable values derived from the scene's digital data. As an example,time of day could be a digital time stamp obtained from a cell phone'sinternal clock or an internet time server. An additional example ofsentiment characteristics could include specific GPS coordinates.Sentiment state represents an instantiated data object where thecorresponding state depends on the scene characteristics. In someembodiments, the sentiment state can be instantiated from an a prioridefined class in the object oriented programming sense, where sentimentcharacteristics are passed to a constructor of the class. In otherembodiments, the sentiment state could be a predefined data object(e.g., a template, a database record, etc.) having sentiment criteriathat must be satisfied in order for the state to be considered valid orapplicable.

It should be appreciated that a sentiment characteristic could bedetermined as a valid match to scene data even where each of the itemsassociated with the characteristic (e.g., the items included in { } inFIGS. 3A-3B) is not present. For example, scene data obtained from animage might include “stage” and “alcohol.” Depending on the relevantrule, the scene data may be determined to be a valid match to thesentiment characteristic of a concert even though “many people in area,”“vendor,” “microphone” and “instruments” are not present. Viewed fromanother perspective, the relevant rule could require a threshold numberor percentage of items to be present in a set of scene data to be mappedto a sentiment characteristic.

Similarly, it should be appreciated that a sentiment state could bedetermined as a valid match to sentiment characteristics even where eachof the characteristics associated with the state (e.g., the itemsincluded in { }) is not present.

Ecosystem 100 comprises a device 110 having a digital sensor configuredto capture a digital representation of a scene 105. The device couldcomprise any suitable device such as cell phones, cameras, tablets,phablets, laptop computers, kiosks, audio recorders, and video cameras,and the digital representation could comprise any data including images,videos, or audio that can be captured by a suitable device. Thus,digital representation could comprise one or more digital datamodalities (e.g., image, audio, tactile, temperature, time, location,biometric, heart rate, blood pressure, etc.). Where scene 105 includesobjects, it should be appreciated that the objects could be identifiedand features of the objects could be derived to determine a sentiment ofa user viewing the scene. Additionally or alternatively, where scene 105includes at least a portion of a person (especially a person's face,fingerprint, eye, mouth, stance, pose, etc.), an identity or otherfeatures of the person can be used to determine a sentiment of theperson or a user viewing the person.

Device 110 includes or is communicatively coupled to a digital dataderivation engine 120 programmed to analyze the digital representationand determine, extract or derive scene data from the digitalrepresentation. The digital data derivation engine 120 iscommunicatively coupled to sentiments analysis engine 130, which can beprogrammed to obtain at least one of the digital representation and thescene data from the digital data derivation engine 120. In analternative contemplated embodiment, the sentiments analysis engine 130could obtain the digital representation from device 110 and determine,extract, derive or otherwise obtain scene data via the digitalrepresentation. In yet other contemplated embodiments, two or more ofdevice 110, digital data derivation engine 120, sentiments analysisengine 130, and any other suitable devices or engines could beprogrammed to derive or extract information from the digitalrepresentations to obtain an even more complete set of information(e.g., scene data, etc.) from which to determine a set of sentimentcharacteristics.

Upon obtaining (e.g., receiving, extracting, deriving or determining,etc.) scene data, sentiments analysis engine 130 can query sentimentstates (e.g., sentiment states 144A, 142A, etc.) in a sentimentsdatabase 140. In some embodiments, the sentiments database 140 can beimplemented as a data storage structure such as a relational database(e.g., SQL, Access, etc.), a non-relational database (e.g., NoSQL,etc.), or a spreadsheet that is indexed by the scene data or associatedsentiment characteristics.

Each of the sentiment states is associated with a set of sentimentcharacteristics 144B, 142B, respectively. Viewed from anotherperspective, sentiments analysis engine 130 can obtain or determine aset of sentiment characteristics associated with some or all of theobtained data, and can query the sentiments database 140 (and applyrules on the data retrieved from the sentiments database 140) toassociate the set of sentiment characteristics with one or moresentiment states. It is contemplated that sentiment state data 144A,142A determined by the sentiments analysis engine 130 based on scenedata or sentiment characteristics could comprise a single sentimentstate, a set of sentiment states, or even a ranked set of sentimentstates.

For example, based on a set of sentiment characteristics, the sentimentsanalysis engine 130 can determine that several different sentimentstates, some of which may even conflict with one another (e.g., happy,sad, etc.), are relevant to the scene. The sentiments analysis engine130 can then rank these sentiment states according to their relevancy toa targeted offeree, or other target of interest. In one example wherethe sentiments analysis engine 130 determines that the scene includesthree people with happy faces and one person with a sad face, thesentiments analysis engine 130 can determine that the sentiment state of“happy” is more relevant to the targeted offeree than the sentimentstate of “sad,” and thus rank “happy” higher than “sad.” Furthermore,additional information can be used to provide context, in assistance ofranking the sentiment state. In the same example described above, wherethe targeted offeree is a person capturing the scene, “sad” may beranked higher than “happy,” for example, if it is determined that theperson with the sad face is emotionally closer (e.g., a spouse, etc.),more demographically similar (e.g., same age, etc.) or closer indistance to the targeted offeree relative to the other three people.

The term “sentiment state” is used broadly and includes what istypically referred to as emotions, moods, sentiments, and emotional orpersonality traits, including for example, an emotional state (e.g.,sadness, happiness, depression, anger, fear, nostalgia, joy, disgust,trust, anticipation, surprise, love, friendship, enmity, calmness,confidence, shame, shamelessness, kindness, pity, indignation, envy,etc.), a cognitive state (e.g., readiness, consciousness,unconsciousness, subconscious, curiosity, wonder, confusedness,certainty, doubtfulness, morbidity, preoccupancy, inwardness, etc.) orbody-awareness state (e.g., pain, headache, nausea, etc.).

The term “sentiment characteristic” is used broadly and could comprise asubset of the information or scene data from the digital representation.Additionally or alternatively, a sentiment characteristic could comprisea characteristic derived from the scene data. For example, where adigital representation is of a scene including four faces, scene datacould include “4 people,” and a derived sentiment characteristic couldinclude “smile, smile, frown, smile” (e.g., as represented by imagedata, keywords, numbers, etc.). Based on the information and sentimentcharacteristic, additional sentiment characteristics could be derived,including for example, data representative of a “party” (for anenvironment or event) or “bullying” (for an action or event.

The sentiments analysis engine 130 can comprise or be communicativelycoupled to a commerce analysis engine 150 that is programmed to querycommercial offerings in the commerce database 160. Similar to thesentiments database 140, the commerce database 160 can be implemented asa data storage structure such as a relational database (e.g., SQL,Access, etc.), a non-relational database (e.g., NoSQL, etc.) or aspreadsheet. Under one approach, the commerce database 160 can bestructured to be indexed by sentiment states (e.g., sentiment states144A and 142A, etc.), so that a user or a program can retrieve a set ofrelevant commercial offerings based on a set of sentiment states.

In this example, the commerce database 160 stores multiple commercialofferings (e.g., commercial offerings 164C and 162C, etc.). Each of thecommercial offerings is associated with (e.g., linked from, etc.) a setof sentiment states (e.g., sentiment states 144A and 142A, etc.). Viewedfrom another perspective, commerce analysis engine 150 can obtain dataassociated with one or more sentiment states 144A, 142A from sentimentsanalysis engine 130, and can query the commerce database 160, which canstore rules that can be used to associate the one or more sentimentstates with one or more commercial offerings. Where the commerceanalysis engine 150 is distal from the sentiments analysis engine 130,sentiments analysis engine 130 can transmit one or more determinedsentiment states to commerce analysis engine 150. The results setcomprising one or more sentiment states can be ranked according to afitness measure indicating to what degrees the states satisfy the query.The fitness measure could be calculated as a Hamming distant inembodiments where the query comprises a vector. In other embodiments,the fitness measure should be a count of the number of criterion thatmatch between the query and the states.

Upon determining a commercial offering that is related to the digitaldata, commerce analysis engine 150 can provide the commercial offeringto a user of the system, for example, on a display of device 110.Additionally or alternatively, commerce analysis engine can use thecommercial offering to facilitate, initiate or execute a transactionbetween the user or a person in the scene and a vendor associated withthe commercial offering. It should be appreciated that the ranking ofthe sentiment states can be used to match the states with the commercialofferings. For example, commercial offerings might include one or morecriterion that requires a specific fitness measure for the correspondingstate. Further, the disclosed systems could monetize such requirementsby requiring third parties to pay a fee in exchange for ensuring theirofferings, with possible exclusivity, are placed according to a fitnessmeasure schedule; only place the offering when the fitness measure isvery high for example.

It should be appreciated that any suitable commercial offering typecould be offered based on an association with/mapping to any suitablesentiment state(s) as determined by a set of rules, an implementation ofan algorithm, or any other suitable means. For example, a set of rulescan be used to map states of frustration and frivolity to anadvertisement offering a discount on one or more of video games, massageservices from a national chain and boxing equipment.

It should also be appreciated that any suitable digital transaction typecould be facilitated, initiated or executed by a system of the inventivesubject matter, including for example, a sale, a purchase, an exchange,a preview, a download, a streaming, an order, a payment, a hold, alicense, or a lease. As illustrated, commerce analysis engine 150 iscommunicatively coupled to a communications subsystem 170, which couldfurther facilitate a transaction by (1) providing a link (or otheraccess) to a person or a vendor (180A, 180B, 180C), or (2) initiating orexecuting at least one transaction between the relevant persons, vendorsand entities.

FIGS. 2A-2B illustrate a system of the inventive subject matterincluding device 210, sentiments analysis engine 220, characteristicsdatabase 230, sentiments database 240, commerce analysis engine 250 andcommerce database 260. It is contemplated that one or both of thesentiments analysis engine 220 and commerce analysis engine 250 could beoperated as a service distal to at least one of device 210, a personcarrying device 210 and a person in a digital representation captured bydevice 210. As used herein, the term “distal” means situated away by atleast one half of a mile.

System 200 could be used to infer, determine or otherwise obtainmultiple sentiment states represented by a single digital representation222 of a scene 205. Scene 205 includes numerous objects (e.g.,conference table, coffee cup, pens, glasses, dress shirts, etc.) andpeople (e.g., man leaning over, man with a fist on his face, womanleaning over and writing, and man with glasses in mouth, etc.) fromwhich sentiment characteristics can be derived and mapped to multiplesentiment states. Viewed from another perspective, sentiments analysisengine 220 can receive a digital representation 222 of scene 205 viadevice 210 and derive or otherwise obtain scene data 224 a-e related toat least a portion of the digital representation 222. The scene data 224a-e could include, among other things, information related to: (a) acharacteristic of a person's face, eyes, nose, cheek, forehead or mouth;(b) identification of an object in a scene; (c) a time; (d) a weather;(e) a density of objects, persons or demographic; (f) a height; (g) aweight; (h) a position; (i) an orientation; (j) a direction of a gaze;(k) an interaction; (l) a gesture; (m) a number of persons or objects ina scene; (n) a type of object; (o) demographic information; or any othersuitable information. For example, in FIG. 2 , scene data could includethe following: (1) people, (2) sun, (3) bright, (4) one focal point, (5)coffee cup, (6) four dress shirts, (7) skin color, (8) varying height,(9) glasses, and (10) chair.

Sentiments analysis engine 220 could obtain or derive scene data 224 a-eand determine a set of sentiment characteristics 226B, 228B consistentwith the scene data. It is contemplated that this step could beaccomplished by querying characteristics database 230 storing andprogrammed to associate scene data (e.g., 224 a-e, etc.) with one ormore sentiment characteristics 226B, 228B based on a suitable set ofrules. An implementation of one or more algorithms could be executed onscene data to derive sentiment characteristics. For example, where scenedata includes a clock and a number and an arrow, a time of daycharacteristic can be derived. As another example, where scene dataincludes a scale, a person and a number, a weight characteristic can bederived. In this example, scene data (1)-(6) above could be mapped tothe following sentiment characteristics: (1) Posture=leaning forwardtowards a single table; (2) focal point=document on table; (3) Time ofday=daytime; (4) Gender=male, male, female, male; (5) Hair length=shorthair, short hair, long hair, short hair; and (6) Environment=Office.

Additionally or alternatively, it is contemplated that this step couldbe accomplished without the use of a database. For example, a systemsmanager could manually match scene data to certain sentimentcharacteristics. As another example, a characteristic could simply bescene data or information of a type that appears a threshold number oftimes. For example, where information or scene data from a digitalrepresentation includes (1) people, (2) 7:00 AM, (3) bright, (4) onefocal point, (5) coffee cup, (6) dress shirts, (7) a set of height, (8)glasses, and (9) seated, the system could be configured to recognizethat data relating to daytime appears three times (7:00 AM, bright,coffee cup), and thus determine that daytime is a sentimentcharacteristics.

Once sentiment characteristics are determined, sentiments analysisengine 220 could use the characteristics to query a sentiments database240 that is configured to associate sentiment characteristics with oneor more sentiment states 230 a, 232 a. For example, “daylight” and othersentiment characteristics determined based on the scene data above couldbe associated with the following sentiment states: (1) conferencing; (2)focused; (3) bored; (4) pensive; (5) tired; (6) debating; (7) eager; (8)stressed; (9) self-conscious; (10) annoyed, or any other suitablesentiment states. Further, in some embodiments, each state could bebound to a specific item or topic of interest, possibly an item in thescene. This approach is considered advantageous because it allowsfurther refinement with respect to placing commercial offerings thata 1) relevant to the sentiment state, and 2) relevant to the item ofinterest in the scene.

It should be appreciated that a sentiments analysis engine 220 could beconfigured to determine any suitable number of sentiment characteristics(e.g., at least 1, at least 5, at least 10, at least 15, at least 20, oreven 25 or more, etc.) and sentiment states (e.g., at least 1, at least5, at least 10, at least 15, at least 20, or even 25 or more, etc.)consistent therewith from the extracted scene data. There does not needto be a one-to-one correlation between the number of determinedsentiment characteristics and determined sentiment states. For example,there could be a many-to-one correlation or a one-to-many correlation.Where there are multiple sentiment states determined to be consistentwith a set of sentiment characteristics related to a digitalrepresentation, it is contemplated that a commerce database could beprogrammed to associate a single commercial offering with one, two ormany of the sentiment states, possibly based on a fitness measure orother implementations of ranking algorithms as discussed previously.

Upon determining one or more sentiment states that are related to thedigital representation 222, sentiments analysis engine could transmitdata related to the sentiment state 230 a, 232 a to commerce analysisengine 250. Commerce analysis engine 250 could receive the sentimentstates data 230 a, 232 a and query commerce database 260 to determineone or more commercial offerings 270, 280 associated with sentimentstates 230 a, 232 a. Data related to the commercial offering(s) can bepresented to a user in any suitable manner, including for example, on adisplay of device 210, optionally superimposed over a digitalrepresentation 222 of scene 205.

Viewed from another perspective and as illustrated in the example ofFIGS. 2 a-2 b , a system of the inventive subject matter could beconfigured to utilize digital representations showing objects and peoplerepresentative of two or more sentiment states, and facilitate one ormore transactions based on the two or more sentiment states. Forexample, it is contemplated that a digital representation could bematched to three sentiment states, for example, depression, defeat andfocus related to different persons represented in the scene, and an“associated commercial offering” could comprise an offering associatedwith one, two or all of the sentiment states.

The following use cases illustrate some contemplated applications forsystem 200 (or other systems and methods of the inventive subjectmatter).

Education

Some exemplary uses of a system of the inventive subject matter are inthe field of education. A classroom could include one or more deviceshaving one or more digital sensors configured to capture digital imageryand audio in real time during one or more events (e.g., parent-teacherconference, during a class, during detention, etc.).

The digital data could be used to extract, derive or otherwise obtainrelevant scene data, the scene data could be used to determine, via aquery of a characteristics database, a set of sentiment characteristics(e.g., posture=head in between arms, posture=head in hand, posture,sitting up straight; environment=leisurely; action=talking,action=dancing, action=arguing; focal point=a playground viewablethrough a window of the classroom, etc.) and associated sentimentstates. The sentiment states could be associated with one or morepersons in the scene, an observer of the scene or any other person(s)whose sentiments can be implied based on the digital representation. Forexample, one or more of a state of fear, boredom, focus and excitementcould be associated with a particular student, the same or a differentset of states could be associated with a subset of the students in theroom, the same or a different set of states could be associated with thestudents as a whole, and the same or a different set of states could beassociated with the instructor.

The sets of sentiment states could then be mapped to various commercialofferings, and the commercial offerings associated with one or more ofthe particular student, set of students, students as a whole andinstructor could be presented to a user. The commercial offerings couldbe presented automatically based on the capturing of digital data, orcan be presented based on a request of one or more users. For example,where the devices capturing digital data is owned by or otherwiseassociated with the instructor observing the scene, the system couldpresent the instructor with commercial offerings based on a type ofrequest made by the instructor. The request type can be related tolesson planning suggestions, in which case the system may provide acommercial offering of a downloadable worksheet, a streamable lecture ora teacher's guide for purchase that is associated with a determinationthat students in the scene are bored, tired, disinterested, distracted,feeling trapped, or restless.

Healthcare

Another contemplated use of a system of the inventive subject matter isrelated to the field of healthcare. As patients are not known to beentirely forthcoming when it comes to their health background, a datacapturing device can be used in combination with inputs by a physician,nurse or other user to assist a physician in determining a diagnosis,prognosis, prescription or need of a patient.

Where a female patient having severe stomach pain visits a physicianwith her mother, the physician can input relevant patient informationinto a computing device based on a series of questions. For example, thephysician may input information associated with the following: the mombeing present, the female practicing safe sex, the female not havingeaten anything out of the ordinary in the last 24 hours, and the femalehaving had the pain for at least 48 hours. A system of the inventivesubject matter could be programmed to obtain the information input bythe physician, and to also obtain digital data captured by a video/audiorecorder during the questioning. The system can use both the informationprovided by the physician and the digital data to determine sentimentcharacteristics associated with at least one of the patient and thephysician. Some of all of the sentiment characteristics can then be usedto determine sentiment states of nervousness, fearful, dishonesty, anddistraction associated with the patient based on the digital data, andthe set of sentiment states can be mapped to, for example, a commercialoffering of a take-home pregnancy test, an appointment for an ultrasoundor prenatal vitamins.

Gaming

Yet another contemplated use of a system of the inventive subject matteris related to the field of game play. A webcam communicatively coupledto a computing device can be configured to capture data representing oneor more players of a game operated by computer circuitry andincorporating artificial intelligence technologies or characterdevelopment options. The captured data can be used to infer sentimentsof a player in real-time or near real-time, and to generate intelligentbehaviors based at least in part on the player sentiments, or to impactthe characters or storyline of the game being presented to the player.For example, an inspirational storyline could be presented to a playerwhen a state of sadness, depression or defeat is inferred, causing theplayer to become happy or encouraged. Then, upon inferring a state ofhappiness or encouragement, a devastating plot twist could be presentedto the player.

It should be apparent to those skilled in the art that many moremodifications besides those already described are possible withoutdeparting from the inventive concepts herein. The inventive subjectmatter, therefore, is not to be restricted except in the spirit of theappended claims. Moreover, in interpreting both the specification andthe claims, all terms should be interpreted in the broadest possiblemanner consistent with the context. In particular, the terms “comprises”and “comprising” should be interpreted as referring to elements,components, or steps in a non-exclusive manner, indicating that thereferenced elements, components, or steps may be present, or utilized,or combined with other elements, components, or steps that are notexpressly referenced. Where the specification claims refers to at leastone of something selected from the group consisting of A, B, C . . . andN, the text should be interpreted as requiring only one element from thegroup, not A plus N, or B plus N, etc.

As used in the description herein and throughout the claims that follow,the meaning of “a,” “an,” and “the” includes plural reference unless thecontext clearly dictates otherwise. Also, as used in the descriptionherein, the meaning of “in” includes “in” and “on” unless the contextclearly dictates otherwise.

The recitation of ranges of values herein is merely intended to serve asa shorthand method of referring individually to each separate valuefalling within the range. Unless otherwise indicated herein, eachindividual value is incorporated into the specification as if it wereindividually recited herein. All methods described herein can beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context. The use of any and allexamples, or exemplary language (e.g. “such as”, etc.) provided withrespect to certain embodiments herein is intended merely to betterilluminate the invention and does not pose a limitation on the scope ofthe invention otherwise claimed. No language in the specification shouldbe construed as indicating any non-claimed element essential to thepractice of the invention.

As used herein, and unless the context dictates otherwise, the term“coupled to” is intended to include both direct coupling (in which twoelements that are coupled to each other contact each other) and indirectcoupling (in which at least one additional element is located betweenthe two elements). Therefore, the terms “coupled to” and “coupled with”are used synonymously.

What is claimed is:
 1. A computer-implemented method facilitatingtransactions based on sentiment, the method comprising: obtaining, by atleast one processor, a digital representation of an environment via atleast one sensor, wherein the digital representation includes at least alocation of a device; determining, by the at least one processor, a setof sentiment characteristics via execution of at least one recognitionalgorithm on the digital representation, wherein the set of sentimentcharacteristics includes the location of the device; inferring, by theat least one processor, a set of sentiment states from the set ofsentiment characteristics and from the location of the device;identifying, by the at least one processor, a set of offerings from oneor more vendors that are indexed according to at least one sentimentstate within the set of sentiment states and that satisfy one or morefitness measure criterion; and facilitating, by the at least oneprocessor, at least one transaction related to the set of offeringsbetween the device and the one or more vendors.
 2. The method of claim1, wherein the set of offerings includes at least one commercialoffering.
 3. The method of claim 1, wherein the at least one transactioncomprises at least one of the following: a sale, a purchase, anexchange, a preview, a download, a streaming, an order, a payment, ahold, a license, and a lease.
 4. The method of claim 1, furthercomprising presenting the set of offerings on a display of the device.5. The method of claim 4, wherein the presentation of the set ofofferings is at least in near real-time.
 6. The method of claim 1,wherein the location of the device comprises GPS coordinates.
 7. Themethod of claim 1, wherein the digital representation comprises one ormore digital data modalities.
 8. The method of claim 7, wherein the oneor more digital data modalities include at least one of the followingdata modalities: a digital image, a digital video, a digital sound, atime, a biometric, a heart rate, a blood pressure, and tactile data. 9.The method of claim 1, wherein the set of sentiment characteristicscomprise at least one of the following: an attribute-value pair, ameasurable value derived from the digital representation, a time of day,an action, and an event.
 10. The method of claim 1, wherein the at leastone sentiment state represents a model of at least one of the following:a feeling, an emotion, a view, an opinion, an attitude, a thought, amood, a cognitive state, a body awareness state, and a belief.
 11. Themethod of claim 10, wherein the at least one sentiment state representsat least one of the following feelings: a positive feeling, a negativefeeling, and a neutral feeling.
 12. The method of claim 1, wherein thedigital representation comprises a representation of at least a portionof a person.
 13. The method of claim 12, wherein the representation ofat least the portion of the person comprises at least one of thefollowing: a face, a fingerprint, an eye, a mouth, a stance, and a pose.14. The method of claim 1, wherein the set of sentiment states comprisesa ranked list of sentiment states.
 15. The method of claim 14, whereinthe set of sentiment states are ranked according to the one or morefitness measure criterion.
 16. The method of claim 1, wherein the one ormore fitness measure criterion comprises a for-fee fitness measureschedule.
 17. The method of claim 16, wherein the for-fee fitnessmeasure schedule includes an exclusivity fee.
 18. The method of claim16, further comprising presenting the set of offerings from the one ormore vendors according to fees paid by the one or more vendors based onthe for-fee fitness measure schedule.
 19. The method of claim 1, whereinthe one or more fitness measure criterion comprises a count of the oneor more fitness measure criterion satisfied by the set of offerings. 20.The method of claim 1, wherein the at least one sentiment statecomprises a game player sentiment state.